Compositional (version 2.8)

Multivariate kernel density estimation: Multivariate kernel density estimation

Description

Multivariate kernel density estimation.

Usage

mkde(x, h, thumb = "silverman")

Arguments

x

A matrix with Euclidean (continuous) data.

h

The bandwidh value. It can be a single value, which is turned into a vector and then into a diagonal matrix, or a vector which is turned into a diagonal matrix.

thumb

Do you want to use a rule of thumb for the bandwidth parameter? If no, leave it "none", or else put "estim" for maximum likelihood cross-validation, "scott" or "silverman" for Scott's and Silverman's rules of thumb respectively.

Value

A vector with the density estimates calculated for every vector.

Details

The multivariate kernel density estimate is calculated with a (not necssarily given) bandwidth value. It is used a wrapper for the function comp.kerncontour.

References

Arsalane Chouaib Guidoum (2015). Kernel Estimator and Bandwidth Selection for Density and its Derivatives. The kedd package. http://cran.r-project.org/web/packages/kedd/vignettes/kedd.pdf

M.P. Wand and M.C. Jones (1995). Kernel smoothing, pages 91-92.

B.W. Silverman (1986). Density estimation for statistics and data analysis, pages 76-78.

See Also

mkde.tune, comp.kerncontour

Examples

# NOT RUN {
mkde( as.matrix(iris[, 1:4]), thumb = "scott" )
mkde( as.matrix(iris[, 1:4]), thumb = "silverman" )
# }